Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	41,028
2	Louisiana	38,230
3	Mississippi	36,962
4	South Dakota	36,862
5	Florida	35,035
6	Alabama	35,010
7	Iowa	33,817
8	Arkansas	32,613
9	Tennessee	32,596
10	South Carolina	31,699
11	Arizona	31,655
12	Georgia	30,588
13	Wisconsin	30,168
14	Texas	29,781
15	Nebraska	29,639
16	Idaho	29,423
17	Nevada	29,169
18	Utah	29,101
19	Illinois	27,208
20	Oklahoma	26,915
21	Missouri	26,176
22	Rhode Island	26,139
23	New York	25,066
24	Kansas	24,925
25	New Jersey	24,827
26	Delaware	23,560
27	North Carolina	23,266
28	District of Columbia	23,144
29	Maryland	22,406
30	California	22,176
31	Indiana	22,043
32	Minnesota	21,480
33	Montana	20,849
34	Massachusetts	20,737
35	Kentucky	19,881
36	Virginia	19,358
37	Puerto Rico	17,738
38	Connecticut	17,622
39	New Mexico	17,332
40	Alaska	16,007
41	Michigan	15,946
42	Ohio	15,418
43	Wyoming	15,232
44	Colorado	14,726
45	Pennsylvania	14,503
46	Washington	13,401
47	West Virginia	11,048
48	Hawaii	9,959
49	Oregon	9,321
50	New Hampshire	7,078
51	Maine	4,398
52	Vermont	3,086

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	1,012
2	South Dakota	902
3	Montana	612
4	Nebraska	494
5	Wisconsin	470
6	Utah	444
7	Iowa	400
8	Idaho	382
9	Wyoming	368
10	Mississippi	357
11	Arkansas	350
12	Missouri	349
13	Illinois	333
14	Indiana	333
15	Oklahoma	327
16	New Mexico	326
17	Minnesota	304
18	Alaska	276
19	Tennessee	266
20	Kentucky	264
21	Nevada	251
22	Alabama	250
23	North Carolina	229
24	Kansas	223
25	South Carolina	220
26	Colorado	208
27	Ohio	187
28	West Virginia	182
29	Texas	181
30	Florida	168
31	Rhode Island	165
32	Delaware	163
33	Georgia	162
34	Michigan	161
35	Virginia	141
36	Arizona	126
37	Pennsylvania	122
38	Puerto Rico	118
39	Maryland	113
40	Louisiana	108
41	New Jersey	101
42	District of Columbia	95
43	Massachusetts	93
44	Connecticut	90
45	Oregon	90
46	Washington	86
47	New York	84
48	California	77
49	New Hampshire	67
50	Hawaii	62
51	Maine	24
52	Vermont	19

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,824
2	New York	1,694
3	Massachusetts	1,410
4	Connecticut	1,273
5	Louisiana	1,231
6	Rhode Island	1,087
7	Mississippi	1,065
8	District of Columbia	908
9	Arizona	800
10	Illinois	747
11	Florida	741
12	Michigan	733
13	South Carolina	706
14	Georgia	699
15	Delaware	681
16	Maryland	667
17	Pennsylvania	666
18	Texas	604
19	Indiana	581
20	Alabama	568
21	Arkansas	558
22	Nevada	554
23	North Dakota	530
24	Iowa	484
25	New Mexico	443
26	Ohio	433
27	Missouri	430
28	California	429
29	Tennessee	420
30	Minnesota	402
31	Virginia	400
32	Colorado	381
33	North Carolina	377
34	South Dakota	356
35	New Hampshire	342
36	Washington	307
37	Kentucky	305
38	Idaho	297
39	Oklahoma	295
40	Kansas	294
41	Nebraska	287
42	Wisconsin	272
43	Puerto Rico	238
44	Montana	224
45	West Virginia	222
46	Utah	168
47	Oregon	147
48	Hawaii	130
49	Maine	108
50	Wyoming	98
51	Vermont	92
52	Alaska	86

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	14
2	South Dakota	9
3	Alabama	5
4	Arkansas	5
5	Florida	4
6	Georgia	4
7	Indiana	4
8	Montana	4
9	Illinois	3
10	Massachusetts	3
11	Mississippi	3
12	Nebraska	3
13	Oklahoma	3
14	Tennessee	3
15	Arizona	2
16	Idaho	2
17	Iowa	2
18	Kansas	2
19	Kentucky	2
20	Louisiana	2
21	Minnesota	2
22	Missouri	2
23	North Carolina	2
24	South Carolina	2
25	Texas	2
26	Wisconsin	2
27	Alaska	1
28	California	1
29	Delaware	1
30	District of Columbia	1
31	Michigan	1
32	Nevada	1
33	New Hampshire	1
34	New Mexico	1
35	Oregon	1
36	Pennsylvania	1
37	Puerto Rico	1
38	Rhode Island	1
39	Utah	1
40	Virginia	1
41	West Virginia	1
42	Colorado	0
43	Connecticut	0
44	Hawaii	0
45	Maine	0
46	Maryland	0
47	New Jersey	0
48	New York	0
49	Ohio	0
50	Vermont	0
51	Washington	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	169,764	1	99
Chattahoochee	Georgia	157,514	2	99
Trousdale	Tennessee	152,428	3	99
Lafayette	Florida	149,608	4	99
Lake	Tennessee	136,403	5	99
Davidson	Tennessee	44,947	254	91
Richland	South Carolina	39,819	376	88
York	South Carolina	21,962	1403	55
Orange	California	18,404	1702	45
Pierce	Washington	11,315	2411	23

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	5,203	1	99
Kenedy	Texas	4,950	2	99
Emporia city	Virginia	4,863	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	625	791	74
Davidson	Tennessee	468	1123	64
Orange	California	444	1177	62
York	South Carolina	335	1464	53
Pierce	Washington	266	1699	45

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.